「power systems」の検索結果

Expert-level power systems covering power generation, transmission, distribution, power flow analysis, fault analysis, protection, and power electronics.

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Power systems engineering covering grid modeling, power flow analysis, energy storage dispatch, demand response, and electricity market economics. Spans transmission/distribution planning to real-time operations. Use when "power flow|load flow|grid model, energy storage|battery dispatch|ESS, demand response|load management|peak shaving, electricity market|LMP|locational marginal price, grid stability|frequency|voltage, capacity planning|resource adequacy, unit commitment|economic dispatch, transmission|distribution|power system, " mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

DC power flow analysis for power systems. Use when computing power flows using DC approximation, building susceptance matrices, calculating line flows and loading percentages, or performing sensitivity analysis on transmission networks.

日本語の概要は準備中です。原文の説明を表示しています。

DC power flow analysis for power systems. Use when computing power flows using DC approximation, building susceptance matrices, calculating line flows and loading percentages, or performing sensitivity analysis on transmission networks.

日本語の概要は準備中です。原文の説明を表示しています。

Electrical power distribution design for infrastructure: load calculations (NEC 220), conductor sizing (NEC 310.16), transformer/UPS/PDU selection, redundancy architectures (N through 2N+1), DC distribution, and voltage classes 120V-480V. Activates for electrical load calculations, conductor sizing, power equipment selection, data center power design, and redundancy architecture planning.

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Expert-level embedded systems covering microcontrollers, RTOS, peripheral interfaces, low-power design, bare-metal programming, and embedded Linux.

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Use when developing firmware for microcontrollers, implementing RTOS applications, or optimizing power consumption. Invoke for STM32, ESP32, FreeRTOS, bare-metal, power optimization, real-time systems, configure peripherals, write interrupt handlers, implement DMA transfers, debug timing issues.

日本語の概要は準備中です。原文の説明を表示しています。

Framework for demonstrating AI capabilities in legal contexts. Provides detailed personas across tenant law, business contracts, startup disputes, employment claims, and consumer protection with progressive complexity scenarios. Use when: (1) Demonstrating AI-powered legal triage or intake systems, (2) Showcasing responsible AI-assisted client interactions, (3) Training staff on appropriate AI use in legal contexts, (4) Creating realistic scenarios for legal tech presentations, (5) Developing educational materials about AI in legal services, or (6) Testing AI-powered legal information systems in controlled environments.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Embedded Systems Engineer. Use when a task calls for Embedded Systems Engineer judgment. Reasons from hardware timing, interrupt latency, memory maps, and deterministic WCET resource bounds through DWT CYCCNT and Saleae logic-analyzer timing proofs, SWD/JTAG (J-Link/OpenOCD) with CFSR/HFSR/BFAR fault decode, FreeRTOS/Zephyr respecting configMAX_SYSCALL_INTERRUPT_PRIORITY, MISRA C:2012 and ISO 26262 ASIL discipline, Nordic PPK2 power profiling, and MCUboot signed OTA while treating priority inversion, Cortex-M7 uncached-DMA D-cache incoherence, CAN bus-off, I2C NACK storms, and brownout-during-flash-write NVS corruption as first-class failure modes.

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Applies the reasoning of Zhong Lin Wang (nanotechnology pioneer, Georgia Tech) to problems involving energy harvesting, IoT power scaling, sensor networks, and fundamental physics applications. Reach for this skill whenever the user is discussing self-powered systems, scaling distributed hardware, overcoming battery bottlenecks, or translating fundamental scientific phenomena (like static electricity or mechanical strain) into novel engineering applications. It is highly relevant for hardware roadmapping, optoelectronics, piezotronics, and challenging established scientific assumptions (like classical Maxwell's equations) to model dynamic systems.

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Expert-level spacecraft design covering mission analysis, subsystem design, power and thermal management, attitude control, communications, and systems engineering.

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Create polished design artifacts as self-contained HTML: UI mockups, interactive prototypes, wireframes, landing pages, dashboards, app screens, mobile apps, slide decks (a.k.a. PPT / PowerPoint presentations), documents and résumés, animations, 3D objects, research reports, HTML email, diagrams, fliers, and visual explorations. Use whenever the user asks to design, mock up, prototype, wireframe, visualize, explore, or make a PPT/deck for an interface, product screen, user flow, content layout, visual artifact, or pitch/deck concept, even if they do not say "design". Also use to export a deck built with this skill to PowerPoint (PPT/PPTX) — but only decks authored here (deck-stage / this skill's slide-structured HTML), NOT arbitrary HTML, so confirm the target is such a deck first. Also use for setting up, importing, or authoring reusable design systems, UI kits, brand tokens, or component libraries. Harness-agnostic for Claude Code, Cursor, Codex Agent, and similar file-capable agents.

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Build empowered product teams using discovery and delivery dual-track. Use when the user mentions "product discovery", "empowered teams", "feature factory", "opportunity assessment", "product vision", "product strategy", "what should we build", or "our roadmap is just a feature list". Also trigger when restructuring teams away from output-driven models, or deciding what to build next based on outcomes. Covers discovery techniques, team structure, opportunity assessment, vision/strategy, and continuous delivery. For customer interviews, see mom-test. For ongoing discovery systems, see continuous-discovery.

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Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.

日本語の概要は準備中です。原文の説明を表示しています。

Develops firmware for microcontrollers, implements RTOS applications, optimizes power consumption for STM32, ESP32, FreeRTOS, and bare-metal systems.

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Build RAG systems for construction knowledge bases. Create searchable AI-powered construction document systems

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Maxim Design System Intelligence — token architecture (primitive→semantic→component), component library governance, CSS variable systems, Tailwind integration, design-to-code handoff, Storybook, Figma token export. Activate for design system creation, token audits, component specs, dark mode systems, brand-to-token sync. Powered by Maxim behavioral science + Atomic Design + TOGAF governance.

日本語の概要は準備中です。原文の説明を表示しています。

Go is the language of infrastructure. From Docker to Kubernetes to the entire cloud-native ecosystem, Go powers the systems that run the internet. It's not about what you can build - it's about what you won't break at 3 AM. This skill covers idiomatic Go patterns, error handling, concurrency with goroutines and channels, HTTP servers, microservice architecture, and the standard library that makes Go so powerful. Key insight: Go's simplicity is a feature. Fight the urge to abstract. Embrace boring, readable code. 2025 lesson: The teams succeeding with Go are the ones who resist overengineering. A main.go with 500 lines beats a "clean architecture" with 50 packages. Use when "golang, go service, go microservice, goroutine, channels, go http, go api, go backend, gin, fiber, chi router, go concurrency, go, golang, microservices, backend, concurrency, goroutines, channels, http, api" mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Expert in designing and implementing visceral, satisfying combat systems. Masters hitbox/hurtbox design, frame data, combo systems, enemy archetypes, damage feedback, and the invisible craft that makes players feel powerful. Draws from fighting games, character action games (DMC, Bayonetta), and Souls-like design to create combat that is readable, responsive, and endlessly replayable. Use when "combat system, combat design, hitbox, hurtbox, frame data, hitstop, screen shake, input buffer, coyote time, i-frames, invincibility frames, combo system, attack cancel, recovery frames, punishment window, souls-like combat, action game combat, fighting game, damage feedback, enemy design, boss design, attack telegraph, parry system, stamina system, poise system, weapon feel, game feel combat, melee combat, combat juice, combat, action-game, fighting-game, hitbox, frame-data, game-feel, souls-like, character-action, melee, enemy-design, boss-design, combo, parry, i-frames" mentioned.

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The systematic orchestration of AI-powered marketing workflows that combine content generation, approval processes, multi-channel distribution, and quality gates into cohesive automation systems. This skill integrates AI generation tools (Jasper, Claude, GPT) with automation platforms (Zapier, Make, n8n) and marketing systems to build scalable content pipelines. It focuses on maintaining brand consistency, implementing rigorous quality gates, and balancing automation with strategic human oversight. Key capabilities include designing parallel approval flows, monitoring costs, and architecting "invisible" automation that enhances productivity without sacrificing quality.Use when "AI workflow, automate content, content automation, workflow automation, AI pipeline, automated marketing, content distribution automation, approval workflow, scale content production, AI orchestration, automation, workflow, ai-orchestration, content-pipeline, approval-workflow, multi-channel, quality-gates, cost-control" mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.

日本語の概要は準備中です。原文の説明を表示しています。

Benchmark multi-agent coordination systems with experiment design, power analysis, human rating protocols, bootstrap confidence intervals, and reproducible reporting. Use for salvage latency, recovery fidelity, coordination overhead, and protocol comparison studies. NOT for ML model benchmarking, web-product A/B testing, survey design, or general-purpose data science.

日本語の概要は準備中です。原文の説明を表示しています。

Expert-level machine design covering shafts, bearings, gears, fasteners, springs, clutches, brakes, and mechanical power transmission systems.

日本語の概要は準備中です。原文の説明を表示しています。

Maxim Design System Intelligence — token architecture (primitive→semantic→component), component library governance, CSS variable systems, Tailwind integration, design-to-code handoff, Storybook, Figma token export. Activate for design system creation, token audits, component specs, dark mode systems, brand-to-token sync. Powered by Maxim behavioral science + Atomic Design + TOGAF governance.

日本語の概要は準備中です。原文の説明を表示しています。